A well-known stochastic search method is simulated annealing. Simulated annealing is a general optimization technique for solving combinatorial optimization problems (e.g., Travelling Salesperson and VLSI design, c.f. S. Kirkpatrick et al., “Optimization by Simulated Annealing,” Science, Vol. 220 (1983), pp. 671-680), as well as optimization problems involving mixed-integer, continuous and/or semi-continuous variables.
As a continuous optimization method, simulated annealing shows promising results in several cases of non-convex and mixed integer optimization problems.
An extensive overview and survey on simulated annealing can be found in, “Simulated Annealing: Theory and Applications” by P. J. M. van Laarhoven and E. H. L. Aarts, D. Reidel Publishing Company.
Various features of a simulated annealing algorithm, for instance the definition of some type of neighborhood structure (e.g., a topology, a metric, or an adjacency graph) on the feasible region, a temperature schedule (a.k.a. cooling schedule), method(s) of generation of points, acceptance criteria (a.k.a. selection scheme in population-oriented simulated annealing), population sizes, etc., are general concepts that need to be selected accordingly for the specific application one is tackling. The performance of the algorithm depends heavily on a wise selection of such features.
Features of the invention will be apparent from review of the disclosure, drawings and description of the invention below.